It’s not just gun violence that we need to understand. So far, we’ve looked at frequently occurring events like shootings and domestic violence, which means there is – in theory, at least – a lot of data to study. But sometimes crime and violence happen as a one-off event, spreading rapidly through a population with devastating consequences.
On the evening of saturday 6 August 2011, London descended into what would become the first of five nights of looting, arson and violence. Two days earlier, police had shot and killed a suspected gang member in Tottenham, North London, sparking protests that evolved into riots and spread across the city. There would also be rioting in other UK cities, from Birmingham to Manchester.
Crime researcher Toby Davies was living in the London district of Brixton at the time.[51] Although Brixton avoided the violence on the first night of the riots, it would end up being one of the worst affected areas. In the months following the riots, Davies and his colleagues at University College London decided to pick apart how such disorder could develop.[52] Rather than trying to explain how or why a riot starts, the team instead focused on what happens once it gets underway. In their analysis, they divided rioting into three basic decisions. The first was whether a person would participate in the riot or not. The researchers assumed this depended on what was happening nearby – much like a disease epidemic – as well as local socioeconomic factors. Once someone decided to participate, the second decision involved where to riot. Because a lot of the rioting and looting was concentrated in retail areas, the researchers adapted an existing model for how shoppers flow into such locations (several media outlets described the London riots as ‘violent shopping’[53]). Finally, their model included the possibility of arrest once a person arrived at the rioting site. This depended on the relative number of rioters and police, a metric Davies referred to as ‘outnumberedness’.
The model could reproduce some of the broad patterns seen during the 2011 riots – such as the focus on Brixton – but it also showed the complexity of these types of events. Davies points out that the model was only a first step; there’s a lot more that needs to be done in this area of research. One big challenge is the availability of data. In their analysis, the UCL team only had information on the number of arrests for riot-related offences. ‘As you can imagine, it’s a very small and very biased subsample,’ Davies said. ‘It doesn’t capture who could potentially engage in rioting.’ In 2011, the rioters were also more diverse than might be expected, with groups transcending long-standing local rivalries. Still, one of the benefits of a model is that it can explore unusual situations and potential responses. For frequent crimes like burglary, police can introduce control measures, see what happens, then refine their strategy. However, this approach isn’t possible for rare events, which might only spark now and again. ‘Police don’t have riots to practise on every day,’ Davies said.
For a riot to start, there need to be at least some people willing to join. ‘You cannot riot on your own,’ as crime researcher John Pitts put it. ‘A one-man riot is a tantrum.’[54] So how does a riot grow from a single person? In 1978, Mark Granovetter published a now classic study looking at how trouble might take off. He suggested that people might have different thresholds for rioting: a radical person might riot regardless of what others were doing, whereas a conservative individual might only riot if many others were. As an example, Granovetter suggested we imagine 100 people hanging around in a square. One person has a threshold of 0, meaning they’ll riot (or tantrum) even if nobody else does; the next person has a threshold of 1, so they will only riot if at least one other person does; the next person has a threshold of 2, and so on, increasing by one each time. Granovetter pointed out that this situation would lead to an inevitable domino effect: the person with a 0 threshold would start rioting, triggering the person with a threshold of 1, which would trigger the person with a threshold of 2. This would continue until the entire crowd was rioting.
But what if the situation were slightly different? Say the person with a threshold of 1 had a threshold of 2. This time, the first person would start rioting, but there would be nobody else with a low enough threshold to be triggered. Although the crowds in each situation are near identical, the behaviour of one person could be the difference between a riot and a tantrum. Granovetter suggested personal thresholds could apply to other forms of collective behaviour too, from going on strike to leaving a social event.[55]
The emergence of collective behaviour can also be relevant to counter-terrorism. Are potential terrorists recruited into an existing hierarchy, or do they form groups organically? In 2016, physicist Neil Johnson led an analysis looking at how support for the so-called Islamic State grew online. Combing through discussions on social networks, his team found that supporters aggregated in progressively larger groups, before breaking apart into smaller ones when the authorities shut them down. Johnson has compared the process to a school of fish splitting and reforming around predators. Despite gathering into distinct groups, Islamic State supporters didn’t seem to have a consistent hierarchy.[56] In their studies of global insurgency, Johnson and his collaborators have argued that these collective dynamics in terrorist groups could explain why large attacks are so much less frequent than smaller ones.[57]
Although Johnson’s study of Islamic State activity aimed to understand the ecosystem of extremism – how groups form, grow, and dissipate – the media preferred to focus on whether it could accurately predict attacks. Unfortunately, predictions are probably still beyond the reach of such
